Co-localization [1]}, co-segmentation 
[2]}, [3]}, and co-saliency [4]} methods
have the same kind of output as weakly-supervised object localization but they
typically do not utilize negative examples. More recently, several methods were
developed for localizing the common novel object under few-shot
setting [5]}, [6]}, [7]}. Shaban
 [5]} learn a pairwise potential function between
proposals of the base classes and use this pairwise metric to solve a
minimum-energy labeling problem over a bidirectional graphical model to
co-localize novel classes. SILCO [6]} finds the common
object by computing a dense similarity map between each support image and the
query while only exploring the similarity among support images using their
coarse image-level features via a global average pooling. Although using global
average pooling reduces the computation, ignoring the dense similarities among
support images negatively affects the common object localization.
